Overview

Dataset statistics

Number of variables22
Number of observations493
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.9 KiB
Average record size in memory176.3 B

Variable types

Numeric21
Categorical1

Warnings

AVNN is highly correlated with IHR and 6 other fieldsHigh correlation
IHR is highly correlated with AVNN and 4 other fieldsHigh correlation
SDNN is highly correlated with AVNN and 14 other fieldsHigh correlation
SDANN is highly correlated with SDNN and 2 other fieldsHigh correlation
SDNNIDX is highly correlated with AVNN and 11 other fieldsHigh correlation
rMSSD is highly correlated with SDNN and 9 other fieldsHigh correlation
pNN10 is highly correlated with AVNN and 10 other fieldsHigh correlation
pNN20 is highly correlated with AVNN and 11 other fieldsHigh correlation
pNN30 is highly correlated with AVNN and 12 other fieldsHigh correlation
pNN40 is highly correlated with SDNN and 10 other fieldsHigh correlation
pNN50 is highly correlated with SDNN and 10 other fieldsHigh correlation
tot_pwr is highly correlated with SDNN and 12 other fieldsHigh correlation
ULF is highly correlated with SDNN and 2 other fieldsHigh correlation
VLF is highly correlated with AVNN and 9 other fieldsHigh correlation
LF is highly correlated with SDNN and 8 other fieldsHigh correlation
HF is highly correlated with SDNN and 8 other fieldsHigh correlation
LF_HF is highly correlated with LF_n and 1 other fieldsHigh correlation
LF_n is highly correlated with LF_HF and 1 other fieldsHigh correlation
HF_n is highly correlated with LF_HF and 1 other fieldsHigh correlation
AVNN is highly correlated with IHR and 7 other fieldsHigh correlation
IHR is highly correlated with AVNN and 7 other fieldsHigh correlation
SDNN is highly correlated with AVNN and 14 other fieldsHigh correlation
SDANN is highly correlated with SDNN and 2 other fieldsHigh correlation
SDNNIDX is highly correlated with AVNN and 13 other fieldsHigh correlation
rMSSD is highly correlated with SDNN and 10 other fieldsHigh correlation
pNN10 is highly correlated with AVNN and 12 other fieldsHigh correlation
pNN20 is highly correlated with AVNN and 12 other fieldsHigh correlation
pNN30 is highly correlated with AVNN and 12 other fieldsHigh correlation
pNN40 is highly correlated with SDNN and 10 other fieldsHigh correlation
pNN50 is highly correlated with SDNN and 10 other fieldsHigh correlation
tot_pwr is highly correlated with AVNN and 14 other fieldsHigh correlation
ULF is highly correlated with SDNN and 4 other fieldsHigh correlation
VLF is highly correlated with AVNN and 13 other fieldsHigh correlation
LF is highly correlated with SDNN and 10 other fieldsHigh correlation
HF is highly correlated with SDNN and 10 other fieldsHigh correlation
LF_HF is highly correlated with LF_n and 1 other fieldsHigh correlation
LF_n is highly correlated with LF_HF and 1 other fieldsHigh correlation
HF_n is highly correlated with LF_HF and 1 other fieldsHigh correlation
AVNN is highly correlated with IHRHigh correlation
IHR is highly correlated with AVNNHigh correlation
SDNN is highly correlated with SDANN and 7 other fieldsHigh correlation
SDANN is highly correlated with SDNN and 2 other fieldsHigh correlation
SDNNIDX is highly correlated with SDNN and 10 other fieldsHigh correlation
rMSSD is highly correlated with SDNNIDX and 6 other fieldsHigh correlation
pNN10 is highly correlated with SDNNIDX and 7 other fieldsHigh correlation
pNN20 is highly correlated with SDNNIDX and 7 other fieldsHigh correlation
pNN30 is highly correlated with SDNNIDX and 7 other fieldsHigh correlation
pNN40 is highly correlated with SDNN and 8 other fieldsHigh correlation
pNN50 is highly correlated with SDNN and 8 other fieldsHigh correlation
tot_pwr is highly correlated with SDNN and 5 other fieldsHigh correlation
ULF is highly correlated with SDNN and 2 other fieldsHigh correlation
VLF is highly correlated with SDNN and 3 other fieldsHigh correlation
LF is highly correlated with SDNN and 9 other fieldsHigh correlation
HF is highly correlated with SDNNIDX and 7 other fieldsHigh correlation
LF_HF is highly correlated with LF_n and 1 other fieldsHigh correlation
LF_n is highly correlated with LF_HF and 1 other fieldsHigh correlation
HF_n is highly correlated with LF_HF and 1 other fieldsHigh correlation
SDANN is highly correlated with tot_pwr and 2 other fieldsHigh correlation
LF_n is highly correlated with rMSSD and 4 other fieldsHigh correlation
tot_pwr is highly correlated with SDANN and 11 other fieldsHigh correlation
IHR is highly correlated with SDNN and 4 other fieldsHigh correlation
VLF is highly correlated with tot_pwr and 7 other fieldsHigh correlation
SDNN is highly correlated with SDANN and 14 other fieldsHigh correlation
NN_RR is highly correlated with rMSSD and 2 other fieldsHigh correlation
ULF is highly correlated with SDANN and 3 other fieldsHigh correlation
pNN30 is highly correlated with tot_pwr and 11 other fieldsHigh correlation
AVNN is highly correlated with IHR and 5 other fieldsHigh correlation
pNN10 is highly correlated with tot_pwr and 9 other fieldsHigh correlation
rMSSD is highly correlated with LF_n and 12 other fieldsHigh correlation
HF_n is highly correlated with LF_n and 4 other fieldsHigh correlation
HF is highly correlated with LF_n and 13 other fieldsHigh correlation
pNN20 is highly correlated with IHR and 10 other fieldsHigh correlation
SDNNIDX is highly correlated with tot_pwr and 11 other fieldsHigh correlation
pNN40 is highly correlated with tot_pwr and 11 other fieldsHigh correlation
pNN50 is highly correlated with LF_n and 13 other fieldsHigh correlation
LF is highly correlated with tot_pwr and 9 other fieldsHigh correlation
LF_HF is highly correlated with LF_n and 1 other fieldsHigh correlation
Participant is uniformly distributed Uniform
Participant has unique values Unique
SDNN has unique values Unique
SDANN has unique values Unique
rMSSD has unique values Unique
pNN10 has unique values Unique
pNN20 has unique values Unique
pNN30 has unique values Unique
pNN40 has unique values Unique
pNN50 has unique values Unique
tot_pwr has unique values Unique
ULF has unique values Unique
VLF has unique values Unique
LF has unique values Unique
HF has unique values Unique
LF_n has unique values Unique
HF_n has unique values Unique

Reproduction

Analysis started2021-11-05 02:50:58.946809
Analysis finished2021-11-05 02:53:28.687325
Duration2 minutes and 29.74 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Participant
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246
Minimum0
Maximum492
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:28.792336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.6
Q1123
median246
Q3369
95-th percentile467.4
Maximum492
Range492
Interquartile range (IQR)246

Descriptive statistics

Standard deviation142.4611058
Coefficient of variation (CV)0.5791101862
Kurtosis-1.2
Mean246
Median Absolute Deviation (MAD)123
Skewness0
Sum121278
Variance20295.16667
MonotonicityStrictly increasing
2021-11-05T08:23:29.009324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.2%
3391
 
0.2%
3371
 
0.2%
3361
 
0.2%
3351
 
0.2%
3341
 
0.2%
3331
 
0.2%
3321
 
0.2%
3311
 
0.2%
3301
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
01
0.2%
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
ValueCountFrequency (%)
4921
0.2%
4911
0.2%
4901
0.2%
4891
0.2%
4881
0.2%
4871
0.2%
4861
0.2%
4851
0.2%
4841
0.2%
4831
0.2%

Category
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
424 
B
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters493
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A424
86.0%
B69
 
14.0%

Length

2021-11-05T08:23:29.270701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-05T08:23:29.359135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
a424
86.0%
b69
 
14.0%

Most occurring characters

ValueCountFrequency (%)
A424
86.0%
B69
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter493
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A424
86.0%
B69
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A424
86.0%
B69
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A424
86.0%
B69
 
14.0%

NN_RR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct461
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9818250953
Minimum0.50568
Maximum0.999971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:29.480664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.50568
5-th percentile0.8861002
Q10.992511
median0.998159
Q30.999495
95-th percentile0.999909
Maximum0.999971
Range0.494291
Interquartile range (IQR)0.006984

Descriptive statistics

Standard deviation0.05234778878
Coefficient of variation (CV)0.05331681684
Kurtosis31.16677884
Mean0.9818250953
Median Absolute Deviation (MAD)0.001604
Skewness-5.032031136
Sum484.039772
Variance0.00274029099
MonotonicityNot monotonic
2021-11-05T08:23:29.671700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.999972
 
0.4%
0.9985742
 
0.4%
0.9982152
 
0.4%
0.9997862
 
0.4%
0.9996532
 
0.4%
0.9998932
 
0.4%
0.9994192
 
0.4%
0.9998992
 
0.4%
0.9996032
 
0.4%
0.9998812
 
0.4%
Other values (451)473
95.9%
ValueCountFrequency (%)
0.505681
0.2%
0.5587781
0.2%
0.65661
0.2%
0.6849591
0.2%
0.7011621
0.2%
0.7210811
0.2%
0.7641771
0.2%
0.7762051
0.2%
0.7824891
0.2%
0.8082311
0.2%
ValueCountFrequency (%)
0.9999712
0.4%
0.999972
0.4%
0.9999691
0.2%
0.9999681
0.2%
0.9999671
0.2%
0.9999661
0.2%
0.9999652
0.4%
0.9999622
0.4%
0.999962
0.4%
0.9999571
0.2%

AVNN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct491
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean968.1601542
Minimum603.824
Maximum1397.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:29.960126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum603.824
5-th percentile752.2376
Q1883.495
median957.889
Q31055.8
95-th percentile1202.762
Maximum1397.57
Range793.746
Interquartile range (IQR)172.305

Descriptive statistics

Standard deviation133.007471
Coefficient of variation (CV)0.1373816826
Kurtosis0.377399771
Mean968.1601542
Median Absolute Deviation (MAD)85.121
Skewness0.3335038783
Sum477302.956
Variance17690.98734
MonotonicityNot monotonic
2021-11-05T08:23:30.203159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
924.2972
 
0.4%
1114.952
 
0.4%
836.3361
 
0.2%
967.5711
 
0.2%
925.0841
 
0.2%
1079.941
 
0.2%
1074.311
 
0.2%
990.4531
 
0.2%
848.1221
 
0.2%
1003.181
 
0.2%
Other values (481)481
97.6%
ValueCountFrequency (%)
603.8241
0.2%
626.4661
0.2%
648.0551
0.2%
657.4981
0.2%
660.4111
0.2%
662.3741
0.2%
685.0261
0.2%
692.1391
0.2%
692.481
0.2%
715.861
0.2%
ValueCountFrequency (%)
1397.571
0.2%
1391.871
0.2%
1354.321
0.2%
1350.651
0.2%
1345.991
0.2%
1338.761
0.2%
1322.661
0.2%
1298.231
0.2%
1280.141
0.2%
1277.061
0.2%

IHR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct491
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.15907662
Minimum42.93165995
Maximum99.36670288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:31.055537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum42.93165995
5-th percentile49.8854477
Q156.82894488
median62.63773778
Q367.91209911
95-th percentile79.76212212
Maximum99.36670288
Range56.43504293
Interquartile range (IQR)11.08315423

Descriptive statistics

Standard deviation8.840920837
Coefficient of variation (CV)0.1399786271
Kurtosis1.044099598
Mean63.15907662
Median Absolute Deviation (MAD)5.48780493
Skewness0.6325195569
Sum31137.42477
Variance78.16188125
MonotonicityNot monotonic
2021-11-05T08:23:31.198542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.914199662
 
0.4%
53.814072382
 
0.4%
71.741501021
 
0.2%
62.01095321
 
0.2%
64.858974971
 
0.2%
55.558642151
 
0.2%
55.849801271
 
0.2%
60.578341431
 
0.2%
70.744539111
 
0.2%
59.809804821
 
0.2%
Other values (481)481
97.6%
ValueCountFrequency (%)
42.931659951
0.2%
43.107474121
0.2%
44.302675881
0.2%
44.423055571
0.2%
44.576854211
0.2%
44.81759241
0.2%
45.363131871
0.2%
46.216772071
0.2%
46.869873611
0.2%
46.982913881
0.2%
ValueCountFrequency (%)
99.366702881
0.2%
95.775349341
0.2%
92.584734321
0.2%
91.255030431
0.2%
90.852514571
0.2%
90.583265651
0.2%
87.587916371
0.2%
86.687789591
0.2%
86.645101661
0.2%
83.815271141
0.2%

SDNN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.42402434
Minimum16.3806
Maximum213.697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:31.662212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum16.3806
5-th percentile44.9554
Q160.2165
median74.7722
Q390.5309
95-th percentile124.8466
Maximum213.697
Range197.3164
Interquartile range (IQR)30.3144

Descriptive statistics

Standard deviation26.13032481
Coefficient of variation (CV)0.3331928581
Kurtosis2.638701366
Mean78.42402434
Median Absolute Deviation (MAD)15.016
Skewness1.166608595
Sum38663.044
Variance682.7938748
MonotonicityNot monotonic
2021-11-05T08:23:32.147723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.93421
 
0.2%
70.01071
 
0.2%
87.89321
 
0.2%
53.34941
 
0.2%
86.95931
 
0.2%
43.83391
 
0.2%
66.94771
 
0.2%
59.9751
 
0.2%
54.71471
 
0.2%
68.64041
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
16.38061
0.2%
27.68191
0.2%
28.17441
0.2%
30.08381
0.2%
31.17641
0.2%
34.42411
0.2%
34.69991
0.2%
36.58891
0.2%
37.48171
0.2%
37.54331
0.2%
ValueCountFrequency (%)
213.6971
0.2%
185.6561
0.2%
177.5041
0.2%
177.3091
0.2%
163.9771
0.2%
163.1671
0.2%
154.5771
0.2%
154.0781
0.2%
153.5031
0.2%
151.0941
0.2%

SDANN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.55825659
Minimum14.3416
Maximum256.855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:32.314379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum14.3416
5-th percentile27.70682
Q140.6135
median50.3163
Q364.1527
95-th percentile92.71214
Maximum256.855
Range242.5134
Interquartile range (IQR)23.5392

Descriptive statistics

Standard deviation22.33015986
Coefficient of variation (CV)0.4092902019
Kurtosis14.69515013
Mean54.55825659
Median Absolute Deviation (MAD)11.001
Skewness2.420212573
Sum26897.2205
Variance498.6360393
MonotonicityNot monotonic
2021-11-05T08:23:32.484358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.56411
 
0.2%
38.92291
 
0.2%
52.4991
 
0.2%
47.95741
 
0.2%
133.1771
 
0.2%
27.65931
 
0.2%
56.77081
 
0.2%
38.23311
 
0.2%
41.75021
 
0.2%
50.07521
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
14.34161
0.2%
15.1461
0.2%
15.50041
0.2%
19.04551
0.2%
20.08471
0.2%
20.46221
0.2%
22.43321
0.2%
22.53121
0.2%
22.94461
0.2%
23.18721
0.2%
ValueCountFrequency (%)
256.8551
0.2%
154.5241
0.2%
134.8841
0.2%
133.1771
0.2%
128.571
0.2%
128.291
0.2%
127.9611
0.2%
125.9591
0.2%
117.2951
0.2%
112.3511
0.2%

SDNNIDX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct492
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.11925619
Minimum6.1648
Maximum187.348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:32.781412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6.1648
5-th percentile25.77092
Q136.9296
median48.2726
Q362.1716
95-th percentile90.78928
Maximum187.348
Range181.1832
Interquartile range (IQR)25.242

Descriptive statistics

Standard deviation21.44233228
Coefficient of variation (CV)0.4114090233
Kurtosis4.280774061
Mean52.11925619
Median Absolute Deviation (MAD)12.1535
Skewness1.493256208
Sum25694.7933
Variance459.7736137
MonotonicityNot monotonic
2021-11-05T08:23:32.924402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.34042
 
0.4%
55.46281
 
0.2%
63.5861
 
0.2%
65.18581
 
0.2%
22.43621
 
0.2%
42.0971
 
0.2%
31.37131
 
0.2%
33.0531
 
0.2%
45.37461
 
0.2%
35.55481
 
0.2%
Other values (482)482
97.8%
ValueCountFrequency (%)
6.16481
0.2%
10.87941
0.2%
18.27831
0.2%
18.49481
0.2%
19.48981
0.2%
20.79541
0.2%
21.27041
0.2%
21.31271
0.2%
21.5851
0.2%
22.17531
0.2%
ValueCountFrequency (%)
187.3481
0.2%
136.2581
0.2%
130.8561
0.2%
126.6031
0.2%
126.5461
0.2%
122.3611
0.2%
120.7081
0.2%
120.4121
0.2%
114.2191
0.2%
112.8381
0.2%

rMSSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.225187
Minimum5.2345
Maximum204.545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:33.167429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5.2345
5-th percentile15.91898
Q123.2348
median31.8363
Q343.648
95-th percentile78.9102
Maximum204.545
Range199.3105
Interquartile range (IQR)20.4132

Descriptive statistics

Standard deviation22.87326635
Coefficient of variation (CV)0.6144567213
Kurtosis11.41976174
Mean37.225187
Median Absolute Deviation (MAD)10.1183
Skewness2.727414812
Sum18352.01719
Variance523.1863136
MonotonicityNot monotonic
2021-11-05T08:23:33.463883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.13871
 
0.2%
49.20351
 
0.2%
30.77841
 
0.2%
11.5241
 
0.2%
33.23781
 
0.2%
25.90331
 
0.2%
23.50341
 
0.2%
32.59021
 
0.2%
24.28471
 
0.2%
30.9381
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
5.23451
0.2%
9.39221
0.2%
9.820331
0.2%
9.927361
0.2%
10.1791
0.2%
11.34461
0.2%
11.41241
0.2%
11.44021
0.2%
11.5241
0.2%
12.49831
0.2%
ValueCountFrequency (%)
204.5451
0.2%
170.3591
0.2%
147.3111
0.2%
146.3681
0.2%
136.4281
0.2%
120.1321
0.2%
119.511
0.2%
117.6391
0.2%
117.1481
0.2%
113.4431
0.2%

pNN10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.1160384
Minimum1.76353
Maximum92.0953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:33.689893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.76353
5-th percentile32.19716
Q150.9752
median65.0593
Q375.0422
95-th percentile84.3252
Maximum92.0953
Range90.33177
Interquartile range (IQR)24.067

Descriptive statistics

Standard deviation16.58321842
Coefficient of variation (CV)0.2669716043
Kurtosis-0.1249973999
Mean62.1160384
Median Absolute Deviation (MAD)11.2689
Skewness-0.6404997875
Sum30623.20693
Variance275.0031333
MonotonicityNot monotonic
2021-11-05T08:23:33.866651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.39311
 
0.2%
75.83421
 
0.2%
60.02941
 
0.2%
33.2571
 
0.2%
73.79091
 
0.2%
61.84731
 
0.2%
61.2951
 
0.2%
65.14711
 
0.2%
68.61741
 
0.2%
71.12171
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
1.763531
0.2%
13.73481
0.2%
14.55761
0.2%
14.9611
0.2%
16.6171
0.2%
22.091
0.2%
22.58171
0.2%
23.18821
0.2%
23.67391
0.2%
26.55591
0.2%
ValueCountFrequency (%)
92.09531
0.2%
90.71161
0.2%
90.49451
0.2%
90.47181
0.2%
90.3691
0.2%
89.70541
0.2%
89.7041
0.2%
89.04591
0.2%
88.96641
0.2%
88.54541
0.2%

pNN20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.74118229
Minimum1.06376
Maximum82.9153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:34.118124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.06376
5-th percentile5.99402
Q118.8012
median33.8657
Q348.9081
95-th percentile67.48242
Maximum82.9153
Range81.85154
Interquartile range (IQR)30.1069

Descriptive statistics

Standard deviation19.35567522
Coefficient of variation (CV)0.5571392204
Kurtosis-0.8038092594
Mean34.74118229
Median Absolute Deviation (MAD)15.0645
Skewness0.2095178705
Sum17127.40287
Variance374.6421631
MonotonicityNot monotonic
2021-11-05T08:23:34.590865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.67751
 
0.2%
51.59331
 
0.2%
28.94831
 
0.2%
5.756151
 
0.2%
41.55231
 
0.2%
29.18561
 
0.2%
26.21821
 
0.2%
37.59851
 
0.2%
37.24231
 
0.2%
42.81571
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
1.063761
0.2%
1.115171
0.2%
1.1941
0.2%
1.710141
0.2%
2.419331
0.2%
2.606721
0.2%
2.655921
0.2%
2.69621
0.2%
2.857281
0.2%
2.996631
0.2%
ValueCountFrequency (%)
82.91531
0.2%
80.3861
0.2%
80.00681
0.2%
79.8611
0.2%
78.70241
0.2%
78.3561
0.2%
77.85671
0.2%
76.91711
0.2%
76.27431
0.2%
75.41721
0.2%

pNN30
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.23110306
Minimum0.346649
Maximum77.9748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:34.858802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.346649
5-th percentile2.036914
Q19.54098
median21.1298
Q335.5197
95-th percentile57.39284
Maximum77.9748
Range77.628151
Interquartile range (IQR)25.97872

Descriptive statistics

Standard deviation17.64681913
Coefficient of variation (CV)0.7282713909
Kurtosis-0.2515064179
Mean24.23110306
Median Absolute Deviation (MAD)12.62374
Skewness0.6825839603
Sum11945.93381
Variance311.4102253
MonotonicityNot monotonic
2021-11-05T08:23:35.152076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.4171
 
0.2%
39.06521
 
0.2%
18.38941
 
0.2%
1.570851
 
0.2%
24.38971
 
0.2%
16.88841
 
0.2%
12.03181
 
0.2%
26.94681
 
0.2%
21.3921
 
0.2%
28.47451
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
0.3466491
0.2%
0.4644581
0.2%
0.4734671
0.2%
0.4792771
0.2%
0.5332281
0.2%
0.5344551
0.2%
0.5777561
0.2%
0.5937011
0.2%
0.6329791
0.2%
0.7725721
0.2%
ValueCountFrequency (%)
77.97481
0.2%
73.96831
0.2%
72.88391
0.2%
72.22481
0.2%
71.05291
0.2%
70.70761
0.2%
70.41771
0.2%
69.99631
0.2%
67.99671
0.2%
66.82151
0.2%

pNN40
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.80681106
Minimum0.0979606
Maximum72.1645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:35.571695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0979606
5-th percentile0.5103564
Q13.61599
median10.2372
Q321.3406
95-th percentile44.99142
Maximum72.1645
Range72.0665394
Interquartile range (IQR)17.72461

Descriptive statistics

Standard deviation14.39512681
Coefficient of variation (CV)0.9721962919
Kurtosis1.45067544
Mean14.80681106
Median Absolute Deviation (MAD)7.8887
Skewness1.339978121
Sum7299.757855
Variance207.2196759
MonotonicityNot monotonic
2021-11-05T08:23:36.042174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.173431
 
0.2%
24.87031
 
0.2%
9.955911
 
0.2%
0.2981711
 
0.2%
9.605671
 
0.2%
7.778971
 
0.2%
3.45361
 
0.2%
16.67411
 
0.2%
7.169321
 
0.2%
14.07461
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
0.09796061
0.2%
0.09999311
0.2%
0.1188511
0.2%
0.1341221
0.2%
0.1789431
0.2%
0.2102441
0.2%
0.2397581
0.2%
0.267711
0.2%
0.2851551
0.2%
0.2951641
0.2%
ValueCountFrequency (%)
72.16451
0.2%
64.80521
0.2%
62.35611
0.2%
61.32471
0.2%
61.2321
0.2%
60.88221
0.2%
60.33611
0.2%
59.60971
0.2%
58.44951
0.2%
55.49971
0.2%

pNN50
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.99858746
Minimum0.0623386
Maximum68.9384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:36.484089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0623386
5-th percentile0.2462776
Q11.90612
median6.42426
Q315.9173
95-th percentile38.67998
Maximum68.9384
Range68.8760614
Interquartile range (IQR)14.01118

Descriptive statistics

Standard deviation12.46220549
Coefficient of variation (CV)1.133073272
Kurtosis3.06311541
Mean10.99858746
Median Absolute Deviation (MAD)5.40787
Skewness1.752005549
Sum5422.30362
Variance155.3065657
MonotonicityNot monotonic
2021-11-05T08:23:36.924072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.441221
 
0.2%
18.21321
 
0.2%
6.854581
 
0.2%
0.1599941
 
0.2%
4.706861
 
0.2%
4.971931
 
0.2%
1.616641
 
0.2%
11.60651
 
0.2%
3.232931
 
0.2%
8.370251
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
0.06233861
0.2%
0.08275291
0.2%
0.09243971
0.2%
0.09629271
0.2%
0.09810011
0.2%
0.1175151
0.2%
0.1353261
0.2%
0.1490361
0.2%
0.1528831
0.2%
0.1528891
0.2%
ValueCountFrequency (%)
68.93841
0.2%
59.02041
0.2%
55.56691
0.2%
55.41031
0.2%
55.37521
0.2%
55.00881
0.2%
54.92841
0.2%
53.81811
0.2%
52.87661
0.2%
47.88621
0.2%

tot_pwr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7604.396846
Minimum283.458
Maximum48863.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:37.270112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum283.458
5-th percentile2089.172
Q13916.66
median6182.74
Q39278.53
95-th percentile18080.02
Maximum48863.7
Range48580.242
Interquartile range (IQR)5361.87

Descriptive statistics

Standard deviation5812.225631
Coefficient of variation (CV)0.764324344
Kurtosis10.30993525
Mean7604.396846
Median Absolute Deviation (MAD)2491.51
Skewness2.608915929
Sum3748967.645
Variance33781966.78
MonotonicityNot monotonic
2021-11-05T08:23:37.531840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5852.651
 
0.2%
5281.041
 
0.2%
8470.371
 
0.2%
3003.971
 
0.2%
9072.731
 
0.2%
2151.881
 
0.2%
4663.131
 
0.2%
3386.711
 
0.2%
3298.391
 
0.2%
5139.661
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
283.4581
0.2%
841.91
0.2%
844.8491
0.2%
875.7621
0.2%
974.9061
0.2%
1209.121
0.2%
1272.481
0.2%
1334.831
0.2%
1431.461
0.2%
1514.941
0.2%
ValueCountFrequency (%)
48863.71
0.2%
38600.21
0.2%
36568.11
0.2%
35459.71
0.2%
33897.81
0.2%
30713.71
0.2%
29969.41
0.2%
28760.71
0.2%
252701
0.2%
25233.41
0.2%

ULF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3788.795093
Minimum229.128
Maximum31032.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:37.688835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum229.128
5-th percentile849.18
Q11853.6
median2908.12
Q34590.61
95-th percentile9509.648
Maximum31032.3
Range30803.172
Interquartile range (IQR)2737.01

Descriptive statistics

Standard deviation3097.535438
Coefficient of variation (CV)0.8175515862
Kurtosis16.04335342
Mean3788.795093
Median Absolute Deviation (MAD)1235.12
Skewness3.008613251
Sum1867875.981
Variance9594725.793
MonotonicityNot monotonic
2021-11-05T08:23:37.839650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2675.951
 
0.2%
1847.311
 
0.2%
3405.811
 
0.2%
2494.111
 
0.2%
6582.251
 
0.2%
883.3061
 
0.2%
3392.271
 
0.2%
1523.371
 
0.2%
1899.581
 
0.2%
2882.411
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
229.1281
0.2%
252.8231
0.2%
282.0831
0.2%
433.9551
0.2%
512.8321
0.2%
537.9721
0.2%
547.241
0.2%
561.5561
0.2%
630.6471
0.2%
640.5211
0.2%
ValueCountFrequency (%)
31032.31
0.2%
19567.61
0.2%
19510.81
0.2%
17863.61
0.2%
16535.81
0.2%
15971.41
0.2%
15522.21
0.2%
13612.61
0.2%
12814.51
0.2%
12241.11
0.2%

VLF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2077.903732
Minimum43.255
Maximum16863.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:38.043983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum43.255
5-th percentile411.0932
Q1943.774
median1582.19
Q32575.98
95-th percentile4926.09
Maximum16863.6
Range16820.345
Interquartile range (IQR)1632.206

Descriptive statistics

Standard deviation1860.045593
Coefficient of variation (CV)0.895154845
Kurtosis13.78220783
Mean2077.903732
Median Absolute Deviation (MAD)740.184
Skewness2.984366195
Sum1024406.54
Variance3459769.609
MonotonicityNot monotonic
2021-11-05T08:23:38.287014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2082.661
 
0.2%
1784.711
 
0.2%
2842.761
 
0.2%
327.9851
 
0.2%
12291
 
0.2%
658.7031
 
0.2%
720.6151
 
0.2%
812.4851
 
0.2%
905.451
 
0.2%
939.271
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
43.2551
0.2%
44.2251
0.2%
101.1271
0.2%
178.1131
0.2%
195.7681
0.2%
275.8221
0.2%
276.021
0.2%
276.2271
0.2%
283.6661
0.2%
283.7911
0.2%
ValueCountFrequency (%)
16863.61
0.2%
12486.61
0.2%
11432.21
0.2%
10662.31
0.2%
10229.91
0.2%
10190.21
0.2%
9500.141
0.2%
9247.411
0.2%
8445.181
0.2%
8209.781
0.2%

LF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1098.025386
Minimum7.75642
Maximum22251.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:38.525014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7.75642
5-th percentile176.8666
Q1398.854
median718.052
Q31296.96
95-th percentile3296.782
Maximum22251.3
Range22243.54358
Interquartile range (IQR)898.106

Descriptive statistics

Standard deviation1449.400257
Coefficient of variation (CV)1.320006145
Kurtosis94.58662094
Mean1098.025386
Median Absolute Deviation (MAD)382.177
Skewness7.49895322
Sum541326.5152
Variance2100761.105
MonotonicityNot monotonic
2021-11-05T08:23:38.679011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
847.9651
 
0.2%
821.5791
 
0.2%
1995.061
 
0.2%
141.8941
 
0.2%
829.2411
 
0.2%
374.4511
 
0.2%
372.851
 
0.2%
741.7851
 
0.2%
260.7581
 
0.2%
819.0581
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
7.756421
0.2%
22.68461
0.2%
59.04841
0.2%
63.86061
0.2%
77.79461
0.2%
81.83371
0.2%
91.39611
0.2%
98.43381
0.2%
100.4921
0.2%
103.3611
0.2%
ValueCountFrequency (%)
22251.31
0.2%
8323.361
0.2%
6952.261
0.2%
6849.381
0.2%
6623.091
0.2%
6454.251
0.2%
6042.671
0.2%
4833.131
0.2%
4806.911
0.2%
4676.681
0.2%

HF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639.6789185
Minimum3.31881
Maximum9287.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:38.926011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.31881
5-th percentile82.98796
Q1177.395
median372.857
Q3659.454
95-th percentile2002.72
Maximum9287.11
Range9283.79119
Interquartile range (IQR)482.059

Descriptive statistics

Standard deviation997.0772697
Coefficient of variation (CV)1.558715226
Kurtosis33.49974022
Mean639.6789185
Median Absolute Deviation (MAD)213.67
Skewness5.096165161
Sum315361.7068
Variance994163.0818
MonotonicityNot monotonic
2021-11-05T08:23:39.075198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246.0771
 
0.2%
827.4511
 
0.2%
226.7331
 
0.2%
39.9781
 
0.2%
432.241
 
0.2%
235.4151
 
0.2%
177.3951
 
0.2%
309.0691
 
0.2%
232.6031
 
0.2%
498.9181
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
3.318811
0.2%
23.44691
0.2%
27.96741
0.2%
28.16441
0.2%
33.28421
0.2%
39.9781
0.2%
41.1451
0.2%
42.43891
0.2%
44.28921
0.2%
55.59941
0.2%
ValueCountFrequency (%)
9287.111
0.2%
9169.981
0.2%
8329.541
0.2%
5784.51
0.2%
5702.351
0.2%
5144.651
0.2%
4856.371
0.2%
4817.771
0.2%
4692.531
0.2%
3841.581
0.2%

LF_HF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct492
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.438171456
Minimum0.199845
Maximum10.659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:39.563753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.199845
5-th percentile0.6582946
Q11.18702
median2.04232
Q33.32708
95-th percentile5.43085
Maximum10.659
Range10.459155
Interquartile range (IQR)2.14006

Descriptive statistics

Standard deviation1.674404923
Coefficient of variation (CV)0.6867461754
Kurtosis3.54253995
Mean2.438171456
Median Absolute Deviation (MAD)0.97484
Skewness1.592070174
Sum1202.018528
Variance2.803631845
MonotonicityNot monotonic
2021-11-05T08:23:40.088228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.003242
 
0.4%
3.445931
 
0.2%
1.02281
 
0.2%
8.799161
 
0.2%
3.54931
 
0.2%
1.918471
 
0.2%
1.59061
 
0.2%
2.101811
 
0.2%
2.400061
 
0.2%
1.121041
 
0.2%
Other values (482)482
97.8%
ValueCountFrequency (%)
0.1998451
0.2%
0.2690951
0.2%
0.311611
0.2%
0.3157141
0.2%
0.3239761
0.2%
0.3319151
0.2%
0.3741031
0.2%
0.3876661
0.2%
0.427411
0.2%
0.4330561
0.2%
ValueCountFrequency (%)
10.6591
0.2%
9.568911
0.2%
9.527021
0.2%
9.376541
0.2%
9.371821
0.2%
8.799161
0.2%
8.03311
0.2%
8.030961
0.2%
7.787521
0.2%
7.252581
0.2%

LF_n
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6475612593
Minimum0.166558989
Maximum0.914229228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:40.506765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.166558989
5-th percentile0.3969707146
Q10.542756451
median0.671303177
Q30.768897035
95-th percentile0.8444990924
Maximum0.914229228
Range0.747670239
Interquartile range (IQR)0.226140584

Descriptive statistics

Standard deviation0.1466502561
Coefficient of variation (CV)0.2264654563
Kurtosis-0.2046151625
Mean0.6475612593
Median Absolute Deviation (MAD)0.107166644
Skewness-0.5611185926
Sum319.2477008
Variance0.02150629761
MonotonicityNot monotonic
2021-11-05T08:23:40.656315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7750753631
 
0.2%
0.4982195591
 
0.2%
0.8979504391
 
0.2%
0.7801860651
 
0.2%
0.6573551251
 
0.2%
0.6139889751
 
0.2%
0.6776072481
 
0.2%
0.7058877831
 
0.2%
0.5285338731
 
0.2%
0.6214513771
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
0.1665589891
0.2%
0.2120371181
0.2%
0.23757831
0.2%
0.2399563151
0.2%
0.2446992091
0.2%
0.2492010971
0.2%
0.2722527771
0.2%
0.2793652651
0.2%
0.2994303631
0.2%
0.3021906441
0.2%
ValueCountFrequency (%)
0.9142292281
0.2%
0.9053829031
0.2%
0.9050063631
0.2%
0.9036287831
0.2%
0.9035849511
0.2%
0.8979504391
0.2%
0.8892960931
0.2%
0.8892697551
0.2%
0.8862022391
0.2%
0.8788258211
0.2%

HF_n
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3524387407
Minimum0.085770772
Maximum0.833441011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-11-05T08:23:40.961437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.085770772
5-th percentile0.1555009076
Q10.231102965
median0.328696823
Q30.457243549
95-th percentile0.6030292854
Maximum0.833441011
Range0.747670239
Interquartile range (IQR)0.226140584

Descriptive statistics

Standard deviation0.1466502561
Coefficient of variation (CV)0.4161014075
Kurtosis-0.2046151625
Mean0.3524387407
Median Absolute Deviation (MAD)0.107166644
Skewness0.5611185926
Sum173.7522992
Variance0.02150629761
MonotonicityNot monotonic
2021-11-05T08:23:41.315397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2249246371
 
0.2%
0.5017804411
 
0.2%
0.1020495611
 
0.2%
0.2198139351
 
0.2%
0.3426448751
 
0.2%
0.3860110251
 
0.2%
0.3223927521
 
0.2%
0.2941122171
 
0.2%
0.4714661271
 
0.2%
0.3785486231
 
0.2%
Other values (483)483
98.0%
ValueCountFrequency (%)
0.0857707721
0.2%
0.0946170971
0.2%
0.0949936371
0.2%
0.0963712171
0.2%
0.0964150491
0.2%
0.1020495611
0.2%
0.1107039071
0.2%
0.1107302451
0.2%
0.1137977611
0.2%
0.1211741791
0.2%
ValueCountFrequency (%)
0.8334410111
0.2%
0.7879628821
0.2%
0.76242171
0.2%
0.7600436851
0.2%
0.7553007911
0.2%
0.7507989031
0.2%
0.7277472231
0.2%
0.7206347351
0.2%
0.7005696371
0.2%
0.6978093561
0.2%

Interactions

2021-11-05T08:21:01.949356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:02.207084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:02.699248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:03.099031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:03.375043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:03.802727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:04.285426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:04.679472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:05.135674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:05.590640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:05.827358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:06.238802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:06.658691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:06.962472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:07.356976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:07.615974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:07.962535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:08.164362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:08.376342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:08.759288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:09.195083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:09.494411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:09.642383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:09.832343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:10.238618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:10.373620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:10.493945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:15.846117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:16.209762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:16.596583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:16.994575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:17.166603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:17.324103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:17.678029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:17.871035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:18.088064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:18.384684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:18.709122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:18.901200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:19.100257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:19.329196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:19.678207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:20.149180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:20.580846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:21.098821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:21.661803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:22.130136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:22.277728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:22.524624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:22.848596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:23.176596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:23.612951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:23.733946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:23.963356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:24.461068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:24.870045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:25.001768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:25.414244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:25.590236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:25.868267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:26.095077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:26.226383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:26.358610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:26.591442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:26.707477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:26.818446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:27.039278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:27.399832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:27.800836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:28.175540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:28.484046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:28.644072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:28.975488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:29.133498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:29.284358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:29.441362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:29.579361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:29.692394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:29.807366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:29.972418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:30.400375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:30.736594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:31.112118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:31.547874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:31.729531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:31.933027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:32.198889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:32.329889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:32.446893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:32.591428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:32.706428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:32.988424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:33.123428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:33.239427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:33.587096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:34.260862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:34.473499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:34.856409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:35.284495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:35.421495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:35.822143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:36.262981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:36.560323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:37.005408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:37.371967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:37.697199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:37.828171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:38.077680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:38.363853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:38.697883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:38.880142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:39.227625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:39.544180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:40.012181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:40.183749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:40.291776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:40.723768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:21:41.049807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-11-05T08:22:49.947492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:50.385496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:50.682514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:51.196948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:51.633979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:51.917520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:52.142551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:52.303536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:52.844361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:53.295411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:53.602352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:54.040387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:54.379983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:54.635916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:55.060664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:55.337634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:55.733603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:56.065623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:56.419810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:56.610182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:56.957192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:57.278435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:57.414396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:57.858113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:58.270568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:58.556540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:58.734542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:59.201149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:22:59.681151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:00.101989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:00.469581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:00.724796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:01.124779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:02.303884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:02.650229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:02.756224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:03.062190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:03.517161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:03.930355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:04.191366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:04.545877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:04.832129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:05.228256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:05.482213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:05.593783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:05.709780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:05.854782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:06.145716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:06.426733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:06.899558image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:07.317817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:07.742153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:07.940094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:08.106276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:08.479936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:08.631884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:08.735417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:09.020073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:09.415077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:09.883698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:10.342600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:10.794375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:11.217555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:11.569237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:11.724217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:12.119455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:12.346442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:12.451442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:12.673174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:12.892478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:13.299285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:13.686251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:13.843259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:13.957259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:14.152489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:14.578184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:14.906396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:15.164154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:15.600709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:15.816690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:15.970559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:16.124158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:16.383161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:16.802309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:17.002953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:17.213907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:17.521604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:17.885571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:18.183563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:18.692549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:19.022664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:19.442226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:19.877157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:20.010155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:20.134160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:20.263195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:20.465222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:20.685195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:20.933193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:21.278962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:21.547753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:21.765766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:21.997294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:22.154300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:22.474442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:22.717323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:23.018318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:23.556928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:23.870900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:23.987869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:24.179547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:24.620653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:25.083758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:25.412641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:25.586665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:26.082579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:26.267574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:26.438573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:26.787272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:27.001249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-05T08:23:27.141245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-11-05T08:23:41.656512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-05T08:23:42.599224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-05T08:23:43.612604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-05T08:23:44.105365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-05T08:23:27.697317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-05T08:23:28.532330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ParticipantCategoryNN_RRAVNNIHRSDNNSDANNSDNNIDXrMSSDpNN10pNN20pNN30pNN40pNN50tot_pwrULFVLFLFHFLF_HFLF_nHF_n
00.0A0.999970836.33671.74150174.934245.564155.462825.138761.393129.677516.417006.1734303.4412205852.652675.9502082.660847.965246.0773.4459300.7750750.224925
11.0A0.999680980.79461.17492686.463157.336955.685326.938660.964926.466614.115106.1260003.8718608793.634467.6003145.010985.214195.7985.0317900.8342120.165788
22.0A0.9931081000.26059.98440459.756232.539147.899731.058759.454729.559619.1820010.5327007.3909403723.201336.0501322.780802.306262.0583.0615600.7537890.246211
33.0A0.959997787.43876.19647559.222027.016039.253943.536229.728613.720912.5419011.46800010.6820002813.47761.8721339.350361.275350.9711.0293600.5072330.492767
44.0B0.999689973.36661.641767117.969067.954389.045373.912886.705272.806463.7922051.80280044.24510014734.806590.4504729.3902354.4801060.5202.2201200.6894520.310548
55.0A0.997305954.35762.86955557.919539.953037.026630.341436.59865.72761.845090.5476590.3129483514.051827.280997.046392.227297.4961.3184300.5686730.431327
66.0A0.998984868.52969.08232289.624668.951348.161327.142067.491737.992324.3289011.4411006.8124309218.966747.3501654.700568.032248.8752.2824000.6953450.304655
77.0A0.996445947.41563.33022073.464549.748051.547256.054877.863154.844742.3573028.37730021.6761005494.492680.2301122.560779.638912.0620.8548080.4608610.539139
88.0A0.992237945.83963.43574386.771664.178144.894219.395043.152010.84524.151821.5464300.9950929063.386180.3702063.660648.006171.3393.7820100.7908830.209117
99.0A0.9986611022.37058.68716881.834469.567435.686725.168664.453533.497820.9529010.0422005.8145507223.356053.930688.519276.095204.8051.3480900.5741210.425879

Last rows

ParticipantCategoryNN_RRAVNNIHRSDNNSDANNSDNNIDXrMSSDpNN10pNN20pNN30pNN40pNN50tot_pwrULFVLFLFHFLF_HFLF_nHF_n
483483.0A0.999150922.72365.02493274.083845.057457.506647.600981.939862.294250.2717034.72950026.1577005669.992317.8701074.820984.5721292.79000.7615870.4323300.567670
484484.0A0.997344906.47466.19053648.449235.370031.043324.383055.334921.24019.985273.4027102.2087802460.101478.530573.056227.556180.95701.2575100.5570350.442965
485485.0A0.9698121114.95053.81407279.101159.899145.522229.843166.883335.726922.6909011.2119007.1504207208.024221.7601854.580764.936366.74202.0857600.6759310.324069
486486.0A0.782489748.90280.11729238.384424.084728.266919.436037.153012.52896.973542.7067501.4101101835.25720.894517.743295.194301.41600.9793570.4947860.505214
487487.0A0.999643915.42165.54361381.102468.401440.875618.501940.591510.14444.045151.0957300.5107936742.434958.5101267.690431.56784.65745.0978100.8360070.163993
488488.0A0.999909896.95666.89291349.696233.722035.595214.108544.005610.07323.196820.6679010.3369872842.361224.2801182.250367.97267.86465.4221500.8442890.155711
489489.0A0.999613960.32162.47910947.844030.982033.301622.329362.025326.547812.244903.5391401.6231802492.801101.070920.625313.928157.17601.9973000.6663670.333633
490490.0B0.999880963.13862.29636964.816852.438039.067019.299856.331022.596211.477604.0000001.7308104990.762560.2201680.300577.137173.10903.3339500.7692640.230736
491491.0A0.977802986.46860.82305845.118235.013629.372420.688162.036626.589313.090904.0744001.9034602256.991260.840587.957232.695175.49001.3259700.5700720.429928
492492.0B0.999761997.28460.16340487.825565.777254.641931.800868.411737.343522.9659010.2238005.7962108871.064803.3402571.0601085.680410.98902.6416300.7253980.274602